#!/usr/bin/env python3
"""Explain one prediction from its SHAP values: a text waterfall and a plain-language summary.
Inputs: a SHAP CSV (feature columns + base_value [+ prediction]) and optionally the aligned feature values.
Pick the row with --row (0-based), or --select highest|lowest to explain the most extreme prediction.
Output:
waterfall base value, the --top contributions ordered by |SHAP| with the feature value, the sum of the
remaining features, and the prediction; with --link logit the log-odds are also shown as
probabilities (the cumulative probability column depends on the order: log-odds add up,
probabilities do not)
summary two or three sentences for a non-technical reader: prediction vs average, the strongest pushes
up and down
check base + sum(SHAP) = prediction for this row (exit 1 if not)
Usage:
python explain_row.py shap_values.csv --features shap_features.csv --row 17 --link logit
python explain_row.py shap_values.csv --features x.csv --select highest --top 5 --label "churn probability"
python explain_row.py shap_values.csv --row 0 --json
Exit codes: 0 explained, 1 additivity mismatch for the row, 2 usage or read error.
"""
import argparse
import json
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from _shap_io import load_shap, sigmoid # noqa: E402
def explain(feats, shap, base, pred, xs, row, top, link):
sv = shap[row]
b = base[row] if base[row] is not None else 0.0
total = b + sum(sv)
p = pred[row] if pred[row] is not None else total
order = sorted(range(len(feats)), key=lambda j: -abs(sv[j]))
items = [{"feature": feats[j], "value": xs[row][j] if xs else None, "shap": sv[j]} for j in order[:top]]
rest = order[top:]
res = {"row": row, "base_value": b, "prediction": p, "sum_check": total, "items": items,
"other_count": len(rest), "other_sum": sum(sv[j] for j in rest), "link": link,
"additive": abs(total - p) <= 1e-3 * max(1.0, abs(p))}
if link == "logit":
res["base_prob"], res["pred_prob"] = sigmoid(b), sigmoid(p)
return res
def show(v):
if v is None:
return ""
try:
f = float(v)
return f"{int(f)}" if f.is_integer() else f"{f:.4g}"
except ValueError:
return str(v) if v != "" else "missing"
def fmt(x):
return f"{x:+.4g}"
def report(r, label):
logit = r["link"] == "logit"
lines = [f"row {r['row']}", ""]
hdr = f"{'':<34} {'SHAP':>9} {'cumulative':>11}" + (f" {'prob':>7}" if logit else "")
lines.append(hdr)
cum = r["base_value"]
lines.append(f"{'base value (average log-odds)' if logit else 'base value (average output)':<34} {'':>9} {cum:>11.4g}" + (f" {sigmoid(cum):>7.1%}" if logit else ""))
for it in r["items"]:
cum += it["shap"]
name = it["feature"] + (f" = {show(it['value'])}" if it["value"] is not None else "")
lines.append(f"{name[:34]:<34} {fmt(it['shap']):>9} {cum:>11.4g}" + (f" {sigmoid(cum):>7.1%}" if logit else ""))
if r["other_count"]:
cum += r["other_sum"]
other = f"{r['other_count']} other features"
lines.append(f"{other:<34} {fmt(r['other_sum']):>9} {cum:>11.4g}"
+ (f" {sigmoid(cum):>7.1%}" if logit else ""))
lines.append(f"{'prediction':<34} {'':>9} {r['prediction']:>11.4g}" + (f" {sigmoid(r['prediction']):>7.1%}" if logit else ""))
lines.append("")
if not r["additive"]:
lines.append(f"CHECK FAILED: base + sum(SHAP) = {r['sum_check']:.6g} but prediction = {r['prediction']:.6g}")
else:
lines.append("check: base + sum(SHAP) = prediction")
lines.append("")
if logit:
head = (f"Predicted {label or 'probability'}: {r['pred_prob']:.1%} for this case, against a baseline of "
f"{r['base_prob']:.1%} (the average log-odds converted to a probability).")
else:
head = (f"Predicted {label or 'value'}: {r['prediction']:.4g} for this case, against an average of "
f"{r['base_value']:.4g}.")
ups = [it for it in r["items"] if it["shap"] > 0][:3]
downs = [it for it in r["items"] if it["shap"] < 0][:3]
unit = " log-odds" if logit else ""
def phrase(its):
return ", ".join((f"{it['feature']} = {show(it['value'])}" if it["value"] is not None else it["feature"])
+ f" ({fmt(it['shap'])}{unit})" for it in its)
lines.append(head)
if ups:
lines.append(f"Pushing it up: {phrase(ups)}.")
if downs:
lines.append(f"Pushing it down: {phrase(downs)}.")
lines.append("These are contributions to this model's output relative to the average case, not causes.")
return "\n".join(lines)
def main():
ap = argparse.ArgumentParser(description="Explain one prediction from SHAP values.", epilog=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("shap_csv")
ap.add_argument("--features")
g = ap.add_mutually_exclusive_group()
g.add_argument("--row", type=int)
g.add_argument("--select", choices=["highest", "lowest"])
ap.add_argument("--top", type=int, default=6)
ap.add_argument("--link", choices=["identity", "logit"], default="identity",
help="logit: SHAP values are log-odds; also show probabilities")
ap.add_argument("--label", help="what the output means, e.g. 'churn probability'")
ap.add_argument("--json", action="store_true")
a = ap.parse_args()
try:
feats, shap, base, pred, xs = load_shap(a.shap_csv, a.features)
except (OSError, ValueError) as e:
print(f"error: {e}", file=sys.stderr)
return 2
n = len(shap)
if a.select:
outs = [(pred[i] if pred[i] is not None else (base[i] or 0.0) + sum(shap[i])) for i in range(n)]
row = max(range(n), key=outs.__getitem__) if a.select == "highest" else min(range(n), key=outs.__getitem__)
else:
row = a.row if a.row is not None else 0
if not 0 <= row < n:
print(f"error: --row {row} out of range (0..{n - 1})", file=sys.stderr)
return 2
r = explain(feats, shap, base, pred, xs, row, a.top, a.link)
print(json.dumps(r, indent=2) if a.json else report(r, a.label))
return 0 if r["additive"] else 1
if __name__ == "__main__":
sys.exit(main())
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